Khanh Dinh, Simon Tavaré, and Zijin Xiang explain the evolution of statistical inference for stochastic processes, presenting ABC-DRF as a solution to longstanding challenges. Distributional random forests, introduced in Cevid et al. (2022), revolutionize regression problems with multi-dimensional dependent variables, and also offer a promising avenue for Bayesian inference. Don't miss the detailed illustration of ABC-DRF methods applied to a compelling toy model, showcasing its potential to reshape the landscape of ABC.
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